CN113688348A - Controllable load distributed coordination control method, device and system based on dynamic network switching topology - Google Patents

Controllable load distributed coordination control method, device and system based on dynamic network switching topology Download PDF

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CN113688348A
CN113688348A CN202110804693.XA CN202110804693A CN113688348A CN 113688348 A CN113688348 A CN 113688348A CN 202110804693 A CN202110804693 A CN 202110804693A CN 113688348 A CN113688348 A CN 113688348A
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load
time
representing
adjustment
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CN113688348B (en
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刘启斌
魏杰
陈征
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Guizhou Wanfeng Electric Power Co ltd
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
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Guizhou Wanfeng Electric Power Co ltd
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Abstract

The invention discloses a controllable load distributed coordination control method, a controllable load distributed coordination control device and a controllable load distributed coordination control system based on dynamic network switching topology, wherein the method comprises the steps of obtaining an adjustable load optimization regulation and control model under the excitation of electricity price; converting the adjustable load optimization regulation and control model into a switching topological structure according to the user participation state, and further obtaining a Lagrangian function; and solving the Lagrangian function to obtain a global optimal solution. The invention fully considers the dynamic characteristics of each controllable load aiming at the problems of numerous and dispersed controllable loads, carries out optimization control on the controllable loads based on a distributed coordination control theory, simultaneously develops the traditional distributed control strategy to a networked distributed control strategy under a dynamic switching topology aiming at the problems of communication network dynamics, topology switching and the like of the controllable loads, solves the problem of network topology dynamic switching which cannot be coped with by the traditional control method, and realizes the safe and stable control of the load side of the power system.

Description

Controllable load distributed coordination control method, device and system based on dynamic network switching topology
Technical Field
The invention belongs to the technical field of power system automation, and particularly relates to a controllable load distributed coordination control method, device and system based on dynamic network switching topology.
Background
Because the power generation capacity of a local power grid is small and has large impact load, the regulation and control requirements brought by the impact load and new energy cannot be met only by the generator, and the load side needs to be effectively controlled to inhibit the influence brought by the fluctuation of the load side. The controllable loads are numerous and distributed, and the traditional control method has great limitation on the communication network dynamics, topology switching and the like of the controllable loads.
Disclosure of Invention
Aiming at the problems, the invention provides a controllable load distributed coordination control method, device and system based on dynamic network switching topology, which can solve the problem that the traditional control method cannot cope with dynamic switching of network topology and realize safe and stable control of the load side of a power system.
In order to achieve the technical purpose and achieve the technical effects, the invention is realized by the following technical scheme:
in a first aspect, the present invention provides a controllable load distributed coordination control method based on a dynamic network switching topology, including:
obtaining an adjustable load optimization regulation and control model under the excitation of electricity price;
converting the adjustable load optimization regulation and control model into a switching topological structure according to the user participation state, and further obtaining a Lagrangian function;
and solving the Lagrangian function to obtain a global optimal solution.
Optionally, the constraint conditions of the adjustable load optimization regulation and control model include:
and (3) load adjustment constraint:
Figure BDA0003165928670000011
wherein, the adjustment load of the user s at the time t
Figure BDA0003165928670000012
Upper and lower limits of
Figure BDA0003165928670000013
t is the load adjustment time t's、t″sRespectively adjusting the upper limit and the lower limit of time for the load of the user s;
participating in adjusting state constraints:
Figure BDA0003165928670000014
wherein the content of the first and second substances,
Figure BDA0003165928670000015
represents the participation of the user s in the adjustment state at the moment t, and adjusts the time t ″s＞t′s。
And (3) load balance constraint:
Figure BDA0003165928670000021
where s represents the s-th user, NsRepresenting the total number of generators, Ps，tFor the amount of load of user s at time t,
Figure BDA0003165928670000022
representing the amount of load that user s takes part in the adjustment at time t.
Optionally, the optimization target of the adjustable load optimization regulation and control model is:
Figure BDA0003165928670000023
wherein, C2Representing a load compensation cost function, s representing the s-th user, NsRepresents the total number of generators, T is the load adjustment time, T represents the total load adjustment time,
Figure BDA0003165928670000024
representing the state of participation of the user s in the adjustment at time t, α1，t、α2，t、α3，tRepresents a compensation factor, α1，t≠0，α2，t，
Figure BDA0003165928670000025
Representing the amount of load the user is engaged in the adjustment at time t.
Optionally, according to the participation state of the user in a certain period, regarding the user as a variable of 0 to 1, if the user participates in the process, setting the participation adjustment state quantity to 1, otherwise setting 0; introduce Lagrange operator, order
Figure BDA0003165928670000026
γ2In order to coordinate the factors, the system is,
Figure BDA0003165928670000027
c2is a control factor, and
Figure BDA0003165928670000028
in conjunction with equation (1), the lagrange function is obtained as:
Figure BDA0003165928670000029
optionally, the solving the lagrangian function to obtain the global optimum specifically includes the following steps:
according to equation (5), Lagrangian function
Figure BDA00031659286700000210
To pair
Figure BDA00031659286700000211
The derivation of the deviation can be derived:
Figure BDA00031659286700000212
order to
Figure BDA00031659286700000213
Let equation (6) equal to 0 result in the optimal adjustment load:
Figure BDA00031659286700000214
the coordination between users is mainly performed by controlling a variable γ, and for a specific user within a fixed time period, it is expressed as:
Figure BDA00031659286700000218
wherein, γs(n +1) represents the control variable of user s at time n +1, γs(n) denotes the control variable, ξ, of the user s at time nsRepresenting the number of iteration steps, s' being a neighbor node of user s,
Figure BDA00031659286700000219
adjacency matrix representing user communication topology, the matrix following participation state
Figure BDA00031659286700000215
Is dynamically switched, wherein the elements a in the adjacency matrix ass′Also affects the communication relations between different users, gammas′(n) represents the control variable of user s' at time n, for any one user s, if it is not involved in load adjustment,that is to say that
Figure BDA00031659286700000216
Then, the weight between neighbors is dynamically adjusted to obtain:
Figure BDA00031659286700000217
wherein, ass′Representing the weight between user s to user s', as′sRepresenting the weight from user s 'to user s, s' being two neighbor nodes of user s, ass″Representing the weight between user s and user s ″, as′s″Represents the weight between user s' to user s ";
coordination among different consumers is represented by a vector version as:
γ(n+1)＝γ(n)+BU(n)γ(n) (10)
wherein the content of the first and second substances,
Figure BDA0003165928670000031
BU＝ξTLU，LUrepresents a laplace matrix with the following variations:
Figure BDA0003165928670000032
and (4) iterating the formula (10) to finally obtain a globally consistent and stable solution.
In a second aspect, the present invention provides a controllable load distributed coordination control apparatus based on a dynamic network switching topology, including:
the acquisition unit is used for acquiring an adjustable load optimization regulation and control model under the excitation of electricity price;
the computing unit is used for converting the adjustable load optimization regulation and control model into a switching topological structure according to the user participation state so as to obtain a Lagrangian function;
and the solving unit is used for solving the Lagrangian function to obtain a global optimal solution.
Optionally, the constraint conditions of the adjustable load optimization regulation and control model include:
and (3) load adjustment constraint:
Figure BDA0003165928670000033
wherein, the adjustment load of the user s at the time t
Figure BDA0003165928670000034
Upper and lower limits of
Figure BDA0003165928670000035
t is the load adjustment time, t ″sAnd t'sRespectively adjusting the upper limit and the lower limit of time for the load of the user s;
participating in adjusting state constraints:
Figure BDA0003165928670000036
wherein the content of the first and second substances,
Figure BDA0003165928670000037
represents the participation of the user s in the adjustment state at the moment t, and adjusts the time t ″s＞t′s。
And (3) load balance constraint:
Figure BDA0003165928670000038
where s represents the s-th user, NsRepresenting the total number of generators, Ps，tFor the amount of load of user s at time t,
Figure BDA0003165928670000039
representing the amount of load that user s takes part in the adjustment at time t.
Optionally, the optimization target of the adjustable load optimization regulation and control model is:
Figure BDA00031659286700000310
wherein, C2Representing a load compensation cost function, s representing the s-th user, NsRepresents the total number of generators, T is the load adjustment time, T represents the total load adjustment time,
Figure BDA00031659286700000311
representing the state of participation of the user s in the adjustment at time t, α1，t、α2，t、α3，tRepresents a compensation factor, α1，t≠0，α2，t，
Figure BDA0003165928670000041
Figure BDA0003165928670000042
Representing the amount of load the user is engaged in the adjustment at time t.
Optionally, according to the participation state of the user in a certain period, regarding the user as a variable of 0 to 1, if the user participates in the process, setting the participation adjustment state quantity to 1, otherwise setting 0; introduce Lagrange operator, order
Figure BDA0003165928670000043
γ2In order to coordinate the factors, the system is,
Figure BDA0003165928670000044
c2is a control factor, and
Figure BDA0003165928670000045
in conjunction with equation (1), the lagrange function is obtained as:
Figure BDA0003165928670000046
optionally, the solving the lagrangian function to obtain the global optimum specifically includes the following steps:
according to equation (5), Lagrangian function
Figure BDA0003165928670000047
To pair
Figure BDA0003165928670000048
The derivation of the deviation can be derived:
Figure BDA0003165928670000049
order to
Figure BDA00031659286700000410
Figure BDA00031659286700000411
Let equation (6) equal to 0 result in the optimal adjustment load:
Figure BDA00031659286700000412
the coordination between users is mainly performed by controlling the variable γ, and for a specific user in a fixed time period, it can be expressed as:
Figure BDA00031659286700000413
wherein, γs(n +1) represents the control variable of user s at time n +1, γs(n) denotes the control variable, ξ, of the user s at time nsRepresenting the number of iteration steps, s' being a neighbor node of user s,
Figure BDA00031659286700000414
adjacency matrix representing user communication topology, the matrix following participation state
Figure BDA00031659286700000415
Is dynamically switched over in accordance with the change of (c),wherein the elements a in the adjacency matrix Ass′Also affects the communication relations between different users, gammas′(n) represents the control variable of the subscriber s' at the time n, which means that for any subscriber s, if it is not involved in the load regulation
Figure BDA00031659286700000416
Then, the weight between neighbors is dynamically adjusted to obtain:
Figure BDA00031659286700000417
wherein, ass′Representing the weight between user s to user s', as′sRepresenting the weight from user s 'to user s, s' being two neighbor nodes of user s, ass″Representing the weight between user s and user s ″, as′s″Represents the weight between user s' to user s ";
coordination among different consumers can be represented in vector versions as:
γ(n+1)＝γ(n)+BU(n)γ(n) (10)
wherein the content of the first and second substances,
Figure BDA00031659286700000418
BU＝ξTLU，LUrepresents a laplace matrix with the following variations:
Figure BDA00031659286700000419
and (4) iterating the formula (10) to finally obtain a globally consistent and stable solution.
In a third aspect, the present invention provides a controllable load distributed coordination control system based on a dynamic network switching topology, including: a storage medium and a processor;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the method according to any one of the first aspects.
Compared with the prior art, the invention has the beneficial effects that:
the invention fully considers the dynamic characteristics of each controllable load aiming at the problems of numerous and dispersed controllable loads, carries out optimization control on the controllable loads based on a distributed coordination control theory, simultaneously develops the traditional distributed control strategy to a networked distributed control strategy under a dynamic switching topology aiming at the problems of communication network dynamics, topology switching and the like of the controllable loads, solves the problem of network topology dynamic switching which cannot be coped with by the traditional control method, and realizes the safe and stable control of the load side of the power system.
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In order that the present disclosure may be more readily and clearly understood, reference is now made to the following detailed description of the present disclosure taken in conjunction with the accompanying drawings, in which:
fig. 1 is a schematic flowchart of a controllable load distributed coordination control based on a dynamic network switching topology according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
Example 1
The invention provides a controllable load distributed coordination control method based on dynamic network switching topology, as shown in fig. 1, specifically comprising the following steps:
(1) obtaining an adjustable load optimization regulation and control model under the excitation of electricity price;
(2) converting the adjustable load optimization regulation and control model into a switching topological structure according to the user participation state, and further obtaining a Lagrangian function;
(3) and solving the Lagrange function by using a distributed coordination control theory and a distributed coordination control method to obtain a global optimal solution.
In a specific implementation manner of the embodiment of the present invention, the constraint condition of the adjustable load optimization regulation and control model includes:
and (3) load adjustment constraint:
Figure BDA0003165928670000051
wherein, the adjustment load of the user s at the time t
Figure BDA0003165928670000061
Upper and lower limits of
Figure BDA0003165928670000062
t is the load adjustment time t'sAnd t ″)sRespectively adjusting the upper limit and the lower limit of time for the load of the user s;
participating in adjusting state constraints:
Figure BDA0003165928670000063
wherein the content of the first and second substances,
Figure BDA0003165928670000064
represents the participation of the user s in the adjustment state at the moment t, and adjusts the time t ″s＞t′s。
And (3) load balance constraint:
Figure BDA0003165928670000065
where s represents the s-th user, NsRepresenting the total number of generators, Ps，tFor the amount of load of user s at time t,
Figure BDA0003165928670000066
representing the load of the user s participating in the adjustment at time tAmount of the compound (A).
The optimization target of the adjustable load optimization regulation and control model is as follows:
Figure BDA0003165928670000067
wherein, C2Representing a load compensation cost function, s representing the s-th user, NsRepresents the total number of generators, T is the load adjustment time, T represents the total load adjustment time,
Figure BDA0003165928670000068
representing the state of participation of the user s in the adjustment at time t, α1，t、α2，t、α3，tRepresents a compensation factor, α1，t≠0，α2，t，
Figure BDA0003165928670000069
Representing the amount of load the user is engaged in the adjustment at time t.
According to the participation state of a user in a certain period, the user is regarded as a variable 0-1, if the user participates in the state, the participation adjustment state quantity is set to be 1, and if the user does not participate in the state, the participation adjustment state quantity is set to be 0; introduce Lagrange operator, order
Figure BDA00031659286700000610
γ2In order to coordinate the factors, the system is,
Figure BDA00031659286700000611
c2is a control factor, and
Figure BDA00031659286700000612
in conjunction with equation (1), the lagrange function is obtained as:
Figure BDA00031659286700000613
the solving of the Lagrangian function to obtain the global optimum specifically comprises the following steps:
according to equation (5), Lagrangian function
Figure BDA00031659286700000614
To pair
Figure BDA00031659286700000615
The derivation of the deviation can be derived:
Figure BDA00031659286700000616
order to
Figure BDA00031659286700000617
Let equation (6) equal to 0 result in the optimal adjustment load:
Figure BDA00031659286700000618
the coordination between users is mainly performed by controlling the variable γ, and for a specific user in a fixed time period, it can be expressed as:
Figure BDA00031659286700000619
wherein, γs(n +1) represents the control variable of user s at time n +1, γs(n) denotes the control variable, ξ, of the user s at time nsRepresenting the number of iteration steps, s' being a neighbor node of user s,
Figure BDA0003165928670000071
adjacency matrix representing user communication topology, the matrix following participation state
Figure BDA0003165928670000072
Is dynamically switched, wherein the elements a in the adjacency matrix ass′Also affects the communication relations between different users, gammas′(n) represents the control variable of user s' at time n, for any oneSubscriber s, if it is not involved in load adjustment, means
Figure BDA0003165928670000073
Then, the weight between neighbors is dynamically adjusted to obtain:
Figure BDA0003165928670000074
wherein, ass′Representing the weight between user s to user s', as′sRepresenting the weight from user s 'to user s, s' being two neighbor nodes of user s, ass″Representing the weight between user s and user s ″, as′s″Representing the weight between user s' to user s ".
Coordination among different consumers can be represented in vector versions as:
γ(n+1)＝γ(n)+BU(n)γ(n) (10)
wherein the content of the first and second substances,
Figure BDA0003165928670000075
BU＝ξTLU，LUrepresents a laplace matrix with the following variations:
Figure BDA0003165928670000076
and (4) iterating the formula (10) to finally obtain a globally consistent and stable solution.
Example 2
Based on the same inventive concept as embodiment 1, the embodiment of the present invention provides a controllable load distributed coordination control apparatus based on a dynamic network switching topology, including:
the acquisition unit is used for acquiring an adjustable load optimization regulation and control model under the excitation of electricity price;
the computing unit is used for converting the adjustable load optimization regulation and control model into a switching topological structure according to the user participation state so as to obtain a Lagrangian function;
and the solving unit is used for solving the Lagrangian function to obtain a global optimal solution.
The constraint conditions of the adjustable load optimization regulation and control model comprise:
and (3) load adjustment constraint:
Figure BDA0003165928670000077
wherein, the adjustment load of the user s at the time t
Figure BDA0003165928670000078
Upper and lower limits of
Figure BDA0003165928670000079
t is the load adjustment time t'sAnd t ″)sAnd respectively adjusting the upper limit and the lower limit of the time for the load of the user s.
Participating in adjusting state constraints:
Figure BDA00031659286700000710
wherein the content of the first and second substances,
Figure BDA00031659286700000711
represents the participation of the user s in the adjustment state at the moment t, and adjusts the time t ″s＞t′s。
And (3) load balance constraint:
Figure BDA0003165928670000081
where s represents the s-th user, NsRepresenting the total number of generators, Ps，tFor the amount of load of user s at time t,
Figure BDA0003165928670000082
representing the participation of user s in the adjustment at time tThe amount of charge.
The optimization target of the adjustable load optimization regulation and control model is as follows:
Figure BDA0003165928670000083
wherein, C2Representing a load compensation cost function, s representing the s-th user, NsRepresents the total number of generators, T is the load adjustment time, T represents the total load adjustment time,
Figure BDA0003165928670000084
representing the state of participation of the user s in the adjustment at time t, α1，t、α2，t、α3，tRepresents a compensation factor, α1，t≠0，α2，t，
Figure BDA0003165928670000085
Figure BDA0003165928670000086
Representing the amount of load the user is engaged in the adjustment at time t.
According to the participation state of a user in a certain period, the user is regarded as a variable 0-1, if the user participates in the state, the participation adjustment state quantity is set to be 1, and if the user does not participate in the state, the participation adjustment state quantity is set to be 0; introduce Lagrange operator, order
Figure BDA0003165928670000087
γ2In order to coordinate the factors, the system is,
Figure BDA0003165928670000088
c2is a control factor, and
Figure BDA0003165928670000089
in conjunction with equation (1), the lagrange function is obtained as:
Figure BDA00031659286700000810
the solving of the Lagrangian function to obtain the global optimum specifically comprises the following steps:
according to equation (5), Lagrangian function
Figure BDA00031659286700000811
To pair
Figure BDA00031659286700000812
The derivation of the deviation can be derived:
Figure BDA00031659286700000813
order to
Figure BDA00031659286700000814
Let equation (6) equal to 0 result in the optimal adjustment load:
Figure BDA00031659286700000815
the coordination between users is mainly performed by controlling the variable γ, and for a specific user in a fixed time period, it can be expressed as:
Figure BDA00031659286700000816
wherein, γs(n +1) represents the control variable of user s at time n +1, γs(n) denotes the control variable, ξ, of the user s at time nsRepresenting the number of iteration steps, s' being a neighbor node of user s,
Figure BDA00031659286700000817
adjacency matrix representing user communication topology, the matrix following participation state
Figure BDA00031659286700000818
Is dynamically switched, wherein the elements a in the adjacency matrix ass′Also affects the communication relations between different users, gammas′(n) represents the control variable of the subscriber s' at the time n, which means that for any subscriber s, if it is not involved in the load regulation
Figure BDA00031659286700000819
Then, the weight between neighbors is dynamically adjusted to obtain:
Figure BDA00031659286700000820
wherein, ass′Representing the weight between user s to user s', as′sRepresenting the weight from user s 'to user s, s' being two neighbor nodes of user s, ass″Representing the weight between user s and user s ″, as′s″Representing the weight between user s' to user s ".
The adjacency matrix A may be rewritten as
Figure BDA0003165928670000091
When the user participates in the load adjustment, the user can switch to different versions. For simplicity, the coordination between different consumers may be represented in vector versions as:
γ(n+1)＝γ(n)+BU(n)γ(n) (10)
wherein the content of the first and second substances,
Figure BDA0003165928670000092
BU＝ξTLU，LUrepresents a laplace matrix with the following variations:
Figure BDA0003165928670000093
and (4) iterating the formula (10) to finally obtain a globally consistent and stable solution.
Example 3
The embodiment of the invention provides a controllable load distributed coordination control device based on dynamic network switching topology, which comprises: a storage medium and a processor;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the method of any of embodiment 1.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (11)

1. A controllable load distributed coordination control method based on dynamic network switching topology is characterized by comprising the following steps:
obtaining an adjustable load optimization regulation and control model under the excitation of electricity price;
converting the adjustable load optimization regulation and control model into a switching topological structure according to the user participation state, and further obtaining a Lagrangian function;
and solving the Lagrangian function to obtain a global optimal solution.
2. The controllable load distributed coordination control method based on dynamic network switching topology according to claim 1, wherein the constraint condition of the adjustable load optimization regulation and control model comprises:
and (3) load adjustment constraint:
Figure FDA0003165928660000011
wherein, the adjustment load of the user s at the time t
Figure FDA0003165928660000012
Upper and lower limits of
Figure FDA0003165928660000013
t is the load adjustment time t's、t″sRespectively adjusting the upper limit and the lower limit of time for the load of the user s;
participating in adjusting state constraints:
Figure FDA0003165928660000014
wherein the content of the first and second substances,
Figure FDA0003165928660000015
represents the participation of the user s in the adjustment state at the moment t, and adjusts the time t ″s＞t′s。
And (3) load balance constraint:
Figure FDA0003165928660000016
where s represents the s-th user, NsRepresenting the total number of generators, Ps，tFor the amount of load of user s at time t,
Figure FDA0003165928660000017
representing the amount of load that user s takes part in the adjustment at time t.
3. The controllable load distributed coordination control method based on the dynamic network switching topology according to claim 1, wherein the optimization goal of the adjustable load optimization regulation model is:
Figure FDA0003165928660000018
wherein, C2To representLoad compensation cost function, s represents the s-th user, NsRepresents the total number of generators, T is the load adjustment time, T represents the total load adjustment time,
Figure FDA0003165928660000019
representing the state of participation of the user s in the adjustment at time t, α1，t、α2，t、α3，tRepresents a compensation factor, α1，t≠0，α2，t，
Figure FDA00031659286600000110
Figure FDA00031659286600000111
Representing the amount of load the user is engaged in the adjustment at time t.
4. The controllable load distributed coordination control method based on the dynamic network switching topology according to claim 3, characterized in that: according to the participation state of a user in a certain period, the user is regarded as a variable 0-1, if the user participates in the state, the participation adjustment state quantity is set to be 1, and if the user does not participate in the state, the participation adjustment state quantity is set to be 0; introduce Lagrange operator, order
Figure FDA00031659286600000112
γ2In order to coordinate the factors, the system is,
Figure FDA00031659286600000113
c2is a control factor, and
Figure FDA00031659286600000114
in conjunction with equation (1), the lagrange function is obtained as:
Figure FDA0003165928660000021
5. the controllable load distributed coordination control method based on dynamic network switching topology according to claim 4,
the method is characterized in that: the solving of the Lagrangian function to obtain the global optimum specifically comprises the following steps:
according to equation (5), Lagrangian function
Figure FDA0003165928660000022
To pair
Figure FDA0003165928660000023
The derivation of the deviation can be derived:
Figure FDA0003165928660000024
order to
Figure FDA0003165928660000025
Let equation (6) equal to 0 result in the optimal adjustment load:
Figure FDA0003165928660000026
the coordination between users is mainly performed by controlling a variable γ, and for a specific user within a fixed time period, it is expressed as:
Figure FDA0003165928660000027
wherein, γs(n +1) represents the control variable of user s at time n +1, γs(n) denotes the control variable, ξ, of the user s at time nsRepresenting the number of iteration steps, s' being a neighbor node of user s,
Figure FDA0003165928660000028
adjacency matrix representing user communication topology, the matrix being dependent on parametersAnd state
Figure FDA0003165928660000029
Is dynamically switched, wherein the elements a in the adjacency matrix ass′Also affects the communication relations between different users, gammas′(n) represents the control variable of the subscriber s' at the time n, which means that for any subscriber s, if it is not involved in the load regulation
Figure FDA00031659286600000210
Then, the weight between neighbors is dynamically adjusted to obtain:
Figure FDA00031659286600000211
wherein, ass′Representing the weight between user s to user s', as′sRepresenting the weight from user s 'to user s, s' being two neighbor nodes of user s, ass″Representing the weight between user s and user s ″, as′s″Represents the weight between user s' to user s ";
coordination among different consumers is represented by a vector version as:
γ(n+1)＝γ(n)+BU(n)γ(n) (10)
wherein the content of the first and second substances,
Figure FDA00031659286600000212
BU＝ξTLU，LUrepresents a laplace matrix with the following variations:
Figure FDA00031659286600000213
and (4) iterating the formula (10) to finally obtain a globally consistent and stable solution.
6. A controllable load distributed coordination control device based on dynamic network switching topology is characterized by comprising:
the acquisition unit is used for acquiring an adjustable load optimization regulation and control model under the excitation of electricity price;
the computing unit is used for converting the adjustable load optimization regulation and control model into a switching topological structure according to the user participation state so as to obtain a Lagrangian function;
and the solving unit is used for solving the Lagrangian function to obtain a global optimal solution.
7. The device according to claim 6, wherein the constraint conditions of the adjustable load optimization regulation and control model include:
and (3) load adjustment constraint:
Figure FDA0003165928660000031
wherein, the adjustment load of the user s at the time t
Figure FDA0003165928660000032
Upper and lower limits of
Figure FDA0003165928660000033
t is the load adjustment time, t ″sAnd t'sRespectively adjusting the upper limit and the lower limit of time for the load of the user s;
participating in adjusting state constraints:
Figure FDA0003165928660000034
wherein the content of the first and second substances,
Figure FDA0003165928660000035
represents the participation of the user s in the adjustment state at the moment t, and adjusts the time t ″s＞t′s。
And (3) load balance constraint:
Figure FDA0003165928660000036
where s represents the s-th user, NsRepresenting the total number of generators, Ps，tFor the amount of load of user s at time t,
Figure FDA0003165928660000037
representing the amount of load that user s takes part in the adjustment at time t.
8. The controllable load distributed coordination control device based on dynamic network switching topology according to claim 7, wherein the optimization goal of the adjustable load optimization regulation and control model is:
Figure FDA0003165928660000038
wherein, C2Representing a load compensation cost function, s representing the s-th user, NsRepresents the total number of generators, T is the load adjustment time, T represents the total load adjustment time,
Figure FDA0003165928660000039
representing the state of participation of the user s in the adjustment at time t, α1，t、α2，t、α3，tRepresents a compensation factor, α1，t≠0，α2，t，
Figure FDA00031659286600000310
Figure FDA00031659286600000311
Representing the amount of load the user is engaged in the adjustment at time t.
9. The apparatus according to claim 8, wherein the user is considered as a variable 0-1 according to the participation status of the user in a certain period, if the user participates in the network, the user will participate in adjusting the status to set 1, otherwise, the user sets 0; introduce Lagrange operator, order
Figure FDA00031659286600000312
γ2In order to coordinate the factors, the system is,
Figure FDA00031659286600000313
c2is a control factor, and
Figure FDA00031659286600000314
in conjunction with equation (1), the lagrange function is obtained as:
Figure FDA00031659286600000315
10. the controllable load distributed coordination control device based on dynamic network switching topology according to claim 9,
the method is characterized in that the Lagrangian function is solved to obtain the global optimum, and the method specifically comprises the following steps:
according to equation (5), Lagrangian function
Figure FDA0003165928660000041
To pair
Figure FDA0003165928660000042
The derivation of the deviation can be derived:
Figure FDA0003165928660000043
order to
Figure FDA0003165928660000044
Let equation (6) equal to 0 result in the optimal adjustment load:
Figure FDA0003165928660000045
the coordination between users is mainly performed by controlling the variable γ, and for a specific user in a fixed time period, it can be expressed as:
Figure FDA0003165928660000046
wherein, γs(n +1) represents the control variable of user s at time n +1, γs(n) denotes the control variable, ξ, of the user s at time nsRepresenting the number of iteration steps, s' being a neighbor node of user s,
Figure FDA0003165928660000047
adjacency matrix representing user communication topology, the matrix following participation state
Figure FDA0003165928660000048
Is dynamically switched, wherein the elements a in the adjacency matrix ass′Also affects the communication relations between different users, gammas′(n) represents the control variable of the subscriber s' at the time n, which means that for any subscriber s, if it is not involved in the load regulation
Figure FDA0003165928660000049
Then, the weight between neighbors is dynamically adjusted to obtain:
Figure FDA00031659286600000410
wherein, ass′Representing the weight between user s to user s', as′sRepresenting the weight from user s 'to user s, s' being two neighbor nodes of user s, ass″Representing the weight between user s and user s ″, as′s″Represents the weight between user s' to user s ";
coordination among different consumers can be represented in vector versions as:
γ(n+1)＝γ(n)+BU(n)γ(n) (10)
wherein the content of the first and second substances,
Figure FDA00031659286600000411
BU＝ξTLU，LUrepresents a laplace matrix with the following variations:
Figure FDA00031659286600000412
and (4) iterating the formula (10) to finally obtain a globally consistent and stable solution.
11. A controllable load distributed coordination control system based on dynamic network switching topology is characterized by comprising: a storage medium and a processor;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the method of any of claims 1-5.
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